The Business of AI
AI has become a business-design question. The relevant issue for a leader is not whether a model can produce an impressive output, but whether a changed workflow creates value under realistic costs, permissions, exceptions and accountability.
The Business of AI is a concise course for experienced operators, consultants and department heads who need to evaluate AI and agentic work without becoming machine-learning specialists. It gives you management artefacts for deciding where AI belongs, how much authority it should receive, what a credible investment case contains and how governance works inside day-to-day operations.
What you will produce
- A Value-to-Evidence Chain for one commercial outcome.
- A Task Portfolio Matrix separating human-led, augmented and bounded automated work.
- An agent cost-to-serve calculation that includes review, exceptions and recovery.
- An operating-model card covering authority, permissions and ownership.
- A one-page Agentic AI Investment Memo for a real situation in your remit.
Course structure
| Module | Lessons | Decision focus |
|---|---|---|
| 1. Value pools and task selection |
|
Where AI can change an owned business outcome |
| 2. Unit economics and operating models |
|
Whether the workflow is affordable, bounded and operable |
| 3. Investment and governance |
|
What evidence and controls justify investment |
| 4. Capstone and ongoing challenge |
|
How to bring the whole decision into one reviewable artefact |
Each lesson ends with a small deliverable. Use one live opportunity throughout the course and the pieces will accumulate into the capstone memo. The examples are illustrative; your baselines, risk classification, permissions and scale thresholds must come from your own operating context and the relevant specialists.
Where this leads
This free course introduces the decision framework. The AI Fluency Certification is the structured, project-based and credentialled next step for professionals who want to demonstrate the capability across commercial, delivery and governance situations.
Frequently asked questions
What is an AI value pool?
An AI value pool is a specific business process where artificial intelligence can generate measurable financial returns. It differs from a generic use case by focusing on quantifiable outcomes, such as reduced labour costs or increased revenue. Identifying these pools allows organisations to prioritise investments based on potential impact rather than technological novelty alone.
How do you calculate the economics of AI agents?
The economics of AI agents are calculated by comparing the total cost of ownership against the value of tasks automated. This includes infrastructure costs, API fees, and human oversight time. A viable agent must generate a positive return on investment, typically by reducing operational expenses or accelerating service delivery beyond the cost of its deployment.
What is an AI operating model?
An AI operating model defines the structure, roles, and workflows required to deploy and maintain artificial intelligence systems. It specifies how data scientists, engineers, and business units collaborate. A clear model ensures that AI initiatives align with broader corporate strategy, providing the necessary governance and resource allocation to sustain long-term performance and accountability.
How do you build an investment case for AI?
Building an investment case for AI requires quantifying expected benefits against implementation costs and risks. It involves defining clear success metrics, such as cost savings or revenue growth, and establishing a timeline for returns. A strong case also addresses potential failure modes and mitigation strategies, ensuring the proposal withstands rigorous financial scrutiny from senior leadership.
Why is AI governance important for businesses?
AI governance is essential to manage risks related to bias, security, and regulatory compliance. It establishes policies for data usage, model validation, and ethical deployment. Without proper governance, organisations face legal liabilities and reputational damage. Effective governance acts as an operating system, ensuring AI tools function safely and align with corporate values and legal requirements.
